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Yonas Tadesse Zinaye
Highlights
Highlights
Breast cancer diagnosis Causal inference testing
Breast cancer diagnosis Causal inference testing
This project use Causal Graph for breast cancer detection based on Breast Cancer Wisconsin (Diagnostic) Data Set. Causal inference refers to an intellectual discipline that considers the assumptions, study designs, and estimation strategies that allow researchers to draw causal conclusions based on data.
In this project, I implemented these main Machine learning and Data Engineering skills:
In this project, I implemented these main Machine learning and Data Engineering skills:
Pandas, Numpy, matplotlib, seaborn, and different python libraries:
Causalnex library:
Jaccard Index of similarity:
Machine learning algorithms used
SPeech-to-text data collection
SPeech-to-text data collection
In this project, a data collection pipeline is built using Kafka, Airflow, and Spark. The pipeline is used to collect readings of transcript for speech to text conversion project. It has a web interface for users to get the transcripts and the pipeline takes it all the way to the data lake by conducting preprocessing.
In this project I implemented the following data pipeline tools
Apache Kafka
Airflow
Spark
AGRITECH - USGS LIDAR
AGRITECH - USGS LIDAR
In this project, python package is developed to fetch and visualize Lidar data for a given area. The USGS recently released high-resolution elevation data as a lidar point cloud called USGS 3DEP in a public dataset on Amazon. This dataset is essential to build models of water flow and predict plant health and maize harvest.
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